A Maximiser of a Linearly Perturbed Semiconvex Function Yields a Subgradient and a Global Quadratic Lower Bound
lemmaAnalysisMultivariable Calculuslem:semiconvex-maximiser-lower-quadratic-bound-2026aIf a semiconvex function with constant has a maximum of its perturbation by a linear form at an interior point of a subset of its domain, then the associated vector is a subgradient of the convexified function there, and the function is bounded below on the whole domain by the corresponding downward parabola.
Let be a natural number with and let be the real numbers with the order of their ordered field structure; denotes , which satisfies and so has a multiplicative inverse by claim 8 of Elementary Order Arithmetic in an Ordered Field, and denotes the product of with that inverse. Regard Euclidean space as a real vector space, with the sum of points, the scalar multiple, and the difference and dot product of points; write for the Euclidean norm and for the Euclidean distance, a metric on with by claim 2 of Elementary Properties of the Euclidean Norm on . Interiors are taken in the topological space formed by and the open sets of , a topology by Metric Open Sets Form a Topology, in the sense of Interior of a Subset of a Topological Space.
Let be convex, let satisfy , and let be semiconvex on with constant ; thus the function given by
is convex on , and its subdifferential relative to at a point is written .
Let , let , and let be an interior point of in such that
Then the following hold.
1. (Subgradient) ¶ ; that is,
2. (Global quadratic lower bound) ¶ For every ,
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